R Notebook
library(lmerTest)
library(ggeffects)
library(dplyr)
library(report)package ‘report’ was built under R version 4.0.5library(r2glmm)fullTest <- read.csv("../Cleaning/output/fullTest.csv")
fullTrain <- read.csv("../Cleaning/output/fullTrain.csv")
traitsFreqs <- read.csv("../Cleaning/output/traitFreqOverUnder.csv")
traitsFreqs <- rename(traitsFreqs, props = optionChoiceN)
fullTest <- merge(fullTest, traitsFreqs[c("trait","props")], by = "trait")
fullTest$propCorr <- ifelse(fullTest$Estimator=="Underestimator", 1-fullTest$props, fullTest$props)
uSubs <- unique(fullTest$subID)
indDiffs <- fullTest[!duplicated(fullTest$subID),]fullTest$ingChoiceN <- as.factor(fullTest$ingChoiceN)
fullTest$novel <- as.factor(fullTest$novel)
fullTest$selfResp.Z <- scale(fullTest$selfResp)
fullTest$SE.Z <- scale(fullTest$SE)
fullTest$iSE.Z <- scale(fullTest$iSE)
fullTest$oSE.Z <- scale(fullTest$oSE)
fullTest$predicted.Z <- scale(fullTest$predicted)
fullTest$slope.Z <- scale(fullTest$slope)
fullTest$entropy.Z <- scale(fullTest$entropy)
fullTest$WSR.Z <- scale(fullTest$WSR)
fullTest$neighAveOutSE.Z <- scale(fullTest$neighAveOutSE)
fullTest$neighAveAllSE.Z <- scale(fullTest$neighAveAllSE)
fullTest$neighAveInSE.Z <- scale(fullTest$neighAveInSE)fullTest$novel <- as.factor(fullTest$novel)
levels(fullTest$novel) <- list("Trained" = "0", "Held Out" = "1")# prop.test(traitsFreqs$optionChoiceN, traitsFreqs$N, p=rep(.5,length(traitsFreqs$N)))
#
# m <- glmer( ingChoiceN ~ trait + ( 1 | subID) , data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
# optCtrl = list(maxfun = 100000)),
# nAGQ = 1)
#
# fullTest$trait <- as.factor(fullTest$trait)
# contrasts(fullTest$trait) <- contr.sum(148)
# m <- glm(optionChoiceN ~ trait, family = binomial,
# data = fullTest
# )
# summary(m)summary(m)
Call:
glm(formula = optionChoiceN ~ 1, family = binomial, data = traitsFreqs)
Deviance Residuals:
Min 1Q Median 3Q Max
-0.81807 -0.21571 0.02476 0.21563 0.82291
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.1913 0.1652 1.158 0.247
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 13.212 on 147 degrees of freedom
Residual deviance: 13.212 on 147 degrees of freedom
AIC: 203.17
Number of Fisher Scoring iterations: 3propMatrix <- matrix(nrow=148,ncol=7)
for(i in 1:148){
traitDf <- subset(fullTest, Idx==i)
test <- t.test(as.numeric(traitDf$ingChoiceN)-1, mu=.50)
propMatrix[i, ] <- c(i, test$statistic, test$p.value, test$conf.int, test$estimate, test$parameter)
}
colnames(propMatrix) <- c("Idx", "stat", "p", "LCI", "UCI", "est", "param")
propMatrix <- as.data.frame(propMatrix)
propMatrix$trait <- traitsFreqs$trait
propMatrix <- propMatrix[order(propMatrix$p),]library(corrr)
x <- indDiffs %>%
select(groupHomoph, DS:SING.Inter) %>%
correlate() %>%
focus(groupHomoph) %>%
arrange(groupHomoph)
Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'x %>%
mutate(rowname = factor(rowname, levels = rowname[order(groupHomoph)])) %>% # Order by correlation strength
ggplot(aes(x = rowname, y = groupHomoph)) +
geom_bar(stat = "identity") +
ylab("Correlation Coefficient") +
xlab("Individual Differences") + theme_grey(base_size = 9) + theme(axis.text.x = element_text(angle = 90,hjust = 1)) +
theme(axis.text.x = element_text(
size = 9, angle = 45, vjust = 1)) + theme(axis.title.x = element_text(vjust=1.9)) + theme(axis.text=element_text(size=9),
axis.title=element_text(size=9,face="bold")) + theme(legend.text = element_text(size=9)) + theme(panel.border = element_rect(colour = "black", fill = NA, size =1)) + theme(legend.title = element_blank()) + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.line = element_line(colour = "black"))
Familiarity predicts Reaction Time
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: log(RT) ~ fam + propCorr + desirability + (fam | subID) + (1 | trait)
Data: fullTest
REML criterion at convergence: 11321
Scaled residuals:
Min 1Q Median 3Q Max
-8.3287 -0.5140 -0.0934 0.4175 7.9841
Random effects:
Groups Name Variance Std.Dev. Corr
trait (Intercept) 8.900e-03 0.094340
subID (Intercept) 4.609e-01 0.678881
fam 2.238e-05 0.004731 1.00
Residual 7.821e-01 0.884369
Number of obs: 4292, groups: trait, 148; subID, 29
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 1.742e-01 2.418e-01 1.360e+02 0.720 0.4727
fam 6.877e-03 3.544e-03 1.169e+02 1.940 0.0548 .
propCorr 8.663e-02 8.951e-02 4.223e+03 0.968 0.3332
desirability -2.723e-02 3.800e-02 1.468e+02 -0.717 0.4747
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) fam prpCrr
fam 0.172
propCorr -0.175 0.030
desirabilty -0.776 -0.393 -0.023
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')Replication of prior self-anchoring findings: Self-evaluations predicting ingroup evaluations
summary(m)
Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: ingChoiceN ~ selfResp.Z + propCorr + desirability + (selfResp.Z | subID) + (1 | trait)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
3050.2 3097.0 -1517.1 3034.2 2548
Scaled residuals:
Min 1Q Median 3Q Max
-4.2611 -0.8119 0.4028 0.7411 4.3393
Random effects:
Groups Name Variance Std.Dev. Corr
trait (Intercept) 0.00000 0.0000
subID (Intercept) 0.47448 0.6888
selfResp.Z 0.09608 0.3100 0.61
Number of obs: 2556, groups: trait, 148; subID, 29
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.45628 0.62618 -2.326 0.0200 *
selfResp.Z 0.27625 0.07804 3.540 0.0004 ***
propCorr 5.00392 0.32287 15.498 <2e-16 ***
desirability -0.12952 0.10317 -1.255 0.2093
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) slfR.Z prpCrr
selfResp.Z 0.183
propCorr -0.204 0.052
desirabilty -0.942 -0.106 -0.054
optimizer (bobyqa) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')Does similarity-weighted self-evaluation average predict ingroup choices?
summary(m)
Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: ingChoiceN ~ WSR.Z + propCorr + desirability + (WSR.Z | subID) + (1 | trait)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
5030.1 5080.9 -2507.1 5014.1 4213
Scaled residuals:
Min 1Q Median 3Q Max
-5.6590 -0.7921 0.3796 0.7276 3.6805
Random effects:
Groups Name Variance Std.Dev. Corr
trait (Intercept) 0.000 0.000
subID (Intercept) 5.967 2.443
WSR.Z 6.912 2.629 0.40
Number of obs: 4221, groups: trait, 148; subID, 29
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.99361 0.69564 -2.866 0.00416 **
WSR.Z 0.74295 0.54509 1.363 0.17288
propCorr 4.83641 0.26082 18.543 < 2e-16 ***
desirability -0.11835 0.08155 -1.451 0.14672
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) WSR.Z prpCrr
WSR.Z 0.289
propCorr -0.158 0.020
desirabilty -0.669 -0.087 -0.053
optimizer (bobyqa) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')Does self-evaluation weighted similarity predict ingroup choices?
Do cross-validated similarity*self-evaluation predictions predict ingroup choices?
Do cross-validated similarity*self-evaluation predictions predict ingroup choices, regardless of whether it was seen prior or not?
m <- glmer( ingChoiceN ~ predicted.Z * novel + ( predicted.Z + novel | subID) , data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)boundary (singular) fit: see help('isSingular')summary(m)Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: ingChoiceN ~ predicted.Z * novel + (predicted.Z + novel | subID)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
4836.3 4898.4 -2408.2 4816.3 3680
Scaled residuals:
Min 1Q Median 3Q Max
-2.2577 -1.0435 0.6154 0.8642 2.1657
Random effects:
Groups Name Variance Std.Dev. Corr
subID (Intercept) 0.297398 0.54534
predicted.Z 0.015375 0.12400 -0.95
novelHeld Out 0.004533 0.06733 0.10 0.22
Number of obs: 3690, groups: subID, 29
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.309950 0.116300 2.665 0.0077 **
predicted.Z 0.259248 0.107170 2.419 0.0156 *
novelHeld Out 0.002235 0.075330 0.030 0.9763
predicted.Z:novelHeld Out -0.053348 0.077558 -0.688 0.4915
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) prdc.Z nvlHlO
predicted.Z -0.321
novelHeldOt -0.308 0.087
prdctd.Z:HO 0.067 -0.289 -0.043
optimizer (bobyqa) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')tidy(m,conf.int=TRUE,exponentiate=TRUE,effects="fixed")r2beta(m)boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular') Effect Rsq upper.CL lower.CL
1 Model 0.012 0.021 0.007
2 predicted.Z 0.008 0.015 0.004
4 predicted.Z:novelHeld Out 0.000 0.002 0.000
3 novelHeld Out 0.000 0.001 0.000ggpredict(m, c("predicted.Z", "novel")) %>% plot(show.title=F)+ xlab("Cross-Validated Self-Descriptiveness") + ylab("Likelihood of Ingroup Choice") + jtools::theme_apa() + scale_color_discrete(labels = c("Trained","Held-Out"))Data were 'prettified'. Consider using `terms="predicted.Z [all]"` to get smooth plots.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.ggsave("~/Documents/UC Riverside/Studies/Self-Anchoring/Figures/CVpredictionGeneralize.tiff",dpi=600)Saving 7.29 x 4.51 in image
Does generalization depend on outdegree?
m <- glmer( ingChoiceN ~ predicted.Z * novel * outDegree + ( predicted.Z + novel | subID) , data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)boundary (singular) fit: see help('isSingular')summary(m)Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: ingChoiceN ~ predicted.Z * novel * outDegree + (predicted.Z + novel | subID)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
4832.9 4919.9 -2402.5 4804.9 3676
Scaled residuals:
Min 1Q Median 3Q Max
-2.3855 -1.0379 0.5867 0.8615 2.2460
Random effects:
Groups Name Variance Std.Dev. Corr
subID (Intercept) 0.29679 0.54479
predicted.Z 0.01238 0.11127 -0.91
novelHeld Out 0.00446 0.06678 0.09 0.33
Number of obs: 3690, groups: subID, 29
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.243639 0.138851 1.755 0.0793 .
predicted.Z 0.291423 0.134287 2.170 0.0300 *
novelHeld Out -0.148255 0.142113 -1.043 0.2968
outDegree 0.003400 0.003735 0.910 0.3627
predicted.Z:novelHeld Out -0.331784 0.143766 -2.308 0.0210 *
predicted.Z:outDegree -0.002504 0.003750 -0.668 0.5043
novelHeld Out:outDegree 0.006894 0.005731 1.203 0.2290
predicted.Z:novelHeld Out:outDegree 0.013065 0.005807 2.250 0.0245 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) prdc.Z nvlHlO outDgr pr.Z:HO pr.Z:D nvHO:D
predicted.Z -0.184
novelHeldOt -0.442 0.064
outDegree -0.555 0.010 0.544
prdctd.Z:HO 0.058 -0.430 0.026 -0.056
prdctd.Z:tD 0.058 -0.577 -0.054 -0.125 0.538
nvlHldOt:tD 0.364 -0.040 -0.847 -0.649 -0.041 0.082
prdc.Z:HO:D -0.035 0.371 -0.038 0.080 -0.844 -0.646 0.040
optimizer (bobyqa) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')tidy(m,conf.int=TRUE,exponentiate=TRUE,effects="fixed")r2beta(m)boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular') Effect Rsq upper.CL lower.CL
1 Model 0.013 0.023 0.008
2 predicted.Z 0.003 0.007 0.000
5 predicted.Z:novelHeld Out 0.002 0.005 0.000
8 predicted.Z:novelHeld Out:outDegree 0.001 0.005 0.000
7 novelHeld Out:outDegree 0.000 0.003 0.000
3 novelHeld Out 0.000 0.002 0.000
4 outDegree 0.000 0.002 0.000
6 predicted.Z:outDegree 0.000 0.002 0.000ggpredict(m, c("predicted.Z", "outDegree" ,"novel")) %>% plot(show.title=F)+ xlab("Cross-Validated Self-Descriptiveness") + ylab("Likelihood of Ingroup Choice") + jtools::theme_apa()Data were 'prettified'. Consider using `terms="predicted.Z [all]"` to get smooth plots.ggsave("~/Documents/UC Riverside/Studies/Self-Anchoring/Figures/CVpredictionGeneralize.tiff",dpi=600)Saving 7.29 x 4.51 in image
Does generalization depend on indegree?
Nope
summary(m)
Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: ingChoiceN ~ predicted.Z * novel * inDegree + (predicted.Z + novel | subID)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
4842.6 4929.6 -2407.3 4814.6 3676
Scaled residuals:
Min 1Q Median 3Q Max
-2.3708 -1.0425 0.6081 0.8478 2.1489
Random effects:
Groups Name Variance Std.Dev. Corr
subID (Intercept) 0.297726 0.54564
predicted.Z 0.014331 0.11971 -0.94
novelHeld Out 0.004817 0.06941 0.06 0.29
Number of obs: 3690, groups: subID, 29
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.3072752 0.1489679 2.063 0.0391 *
predicted.Z 0.2147101 0.1427624 1.504 0.1326
novelHeld Out -0.0106251 0.1574787 -0.067 0.9462
inDegree 0.0001678 0.0045112 0.037 0.9703
predicted.Z:novelHeld Out -0.1367979 0.1585589 -0.863 0.3883
predicted.Z:inDegree 0.0019902 0.0043923 0.453 0.6505
novelHeld Out:inDegree 0.0005728 0.0065070 0.088 0.9298
predicted.Z:novelHeld Out:inDegree 0.0040134 0.0066092 0.607 0.5437
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) prdc.Z nvlHlO inDegr pr.Z:HO pr.Z:D nvHO:D
predicted.Z -0.172
novelHeldOt -0.495 0.045
inDegree -0.626 -0.007 0.598
prdctd.Z:HO 0.043 -0.485 0.019 -0.033
prdctd.Z:nD 0.041 -0.652 -0.035 -0.069 0.585
nvlHldOt:nD 0.437 -0.020 -0.878 -0.693 -0.027 0.048
prdc.Z:HO:D -0.024 0.429 -0.025 0.046 -0.873 -0.666 0.020Neighboring Dependencies Predicting Choices
Generalization of Outdegree Neighboring Self-Evaluations
summary(m)
Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: ingChoiceN ~ neighAveOutSE.Z * novel + desirability + propCorr +
(neighAveOutSE.Z + novel | subID) + (1 | trait)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
4419.5 4500.0 -2196.7 4393.5 3601
Scaled residuals:
Min 1Q Median 3Q Max
-4.1997 -0.8726 0.4282 0.7774 4.3536
Random effects:
Groups Name Variance Std.Dev. Corr
trait (Intercept) 8.505e-15 9.222e-08
subID (Intercept) 3.667e-01 6.056e-01
neighAveOutSE.Z 3.430e-02 1.852e-01 0.44
novelHeld Out 2.598e-02 1.612e-01 0.13 0.95
Number of obs: 3614, groups: trait, 147; subID, 25
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.05530 0.51479 -3.992 6.54e-05 ***
neighAveOutSE.Z 0.23453 0.07600 3.086 0.00203 **
novelHeld Out 0.02107 0.08187 0.257 0.79692
desirability -0.01230 0.08333 -0.148 0.88266
propCorr 4.79310 0.26952 17.784 < 2e-16 ***
neighAveOutSE.Z:novelHeld Out 0.03691 0.08016 0.460 0.64520
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) nAOSE. nvlHlO dsrblt prpCrr
nghAvOtSE.Z 0.105
novelHeldOt -0.057 0.167
desirabilty -0.931 -0.071 0.015
propCorr -0.227 0.045 0.006 -0.035
ngAOSE.Z:HO -0.003 -0.375 0.013 0.007 0.032
optimizer (bobyqa) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')Does entropy (i.e., uncertainty) predict likelihood of ingroup choices?
Does a linear trend of similarity-based probabilities predict ingroup choices?
Does a linear trend of similarity-based probabilities predict ingroup choices, controlling for self-descriptiveness?
m <- glmer( ingChoiceN ~ scale(slope) + selfResp.Z + ( scale(slope) + selfResp.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)boundary (singular) fit: see help('isSingular')summary(m)Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: ingChoiceN ~ scale(slope) + selfResp.Z + (scale(slope) + selfResp.Z | subID) + (1 | trait)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
3324.8 3383.3 -1652.4 3304.8 2546
Scaled residuals:
Min 1Q Median 3Q Max
-3.0960 -1.0011 0.5507 0.8387 2.3694
Random effects:
Groups Name Variance Std.Dev. Corr
trait (Intercept) 0.00000 0.0000
subID (Intercept) 0.38198 0.6180
scale(slope) 0.04406 0.2099 -0.99
selfResp.Z 0.13234 0.3638 0.55 -0.66
Number of obs: 2556, groups: trait, 148; subID, 29
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.27038 0.12721 2.126 0.0335 *
scale(slope) 0.21730 0.11988 1.813 0.0699 .
selfResp.Z 0.19319 0.08783 2.200 0.0278 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) scl(s)
scale(slop) -0.406
selfResp.Z 0.403 -0.454
optimizer (bobyqa) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')tidy(m,conf.int=TRUE,exponentiate=TRUE,effects="fixed")Does a non-parametric trend of similarity-based probabilities predict ingroup choices, controlling for self-descriptiveness?
m <- glmer( ingChoiceN ~ scale(nlslope) + selfResp.Z + ( scale(nlslope) + selfResp.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)boundary (singular) fit: see help('isSingular')summary(m)Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: ingChoiceN ~ scale(nlslope) + selfResp.Z + (scale(nlslope) + selfResp.Z | subID) + (1 | trait)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
3327.3 3385.7 -1653.6 3307.3 2546
Scaled residuals:
Min 1Q Median 3Q Max
-3.0256 -0.9895 0.5363 0.8481 2.3016
Random effects:
Groups Name Variance Std.Dev. Corr
trait (Intercept) 0.0000 0.0000
subID (Intercept) 0.4056 0.6369
scale(nlslope) 0.2145 0.4632 -0.24
selfResp.Z 0.1390 0.3728 0.62 -0.44
Number of obs: 2556, groups: trait, 148; subID, 29
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.35811 0.14766 2.425 0.0153 *
scale(nlslope) 0.17096 0.14329 1.193 0.2328
selfResp.Z 0.20564 0.08781 2.342 0.0192 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) scl(n)
scal(nlslp) -0.073
selfResp.Z 0.393 -0.389
optimizer (bobyqa) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')tidy(m,conf.int=TRUE,exponentiate=TRUE,effects="fixed")Does a linear trend of similarity-based probabilities predict ingroup choices?
m <- glmer( ingChoiceN ~ slope.Z + predicted.Z + ( slope.Z + predicted.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)boundary (singular) fit: see help('isSingular')summary(m)Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: ingChoiceN ~ slope.Z + predicted.Z + (slope.Z + predicted.Z | subID) + (1 | trait)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
4653.3 4715.5 -2316.7 4633.3 3680
Scaled residuals:
Min 1Q Median 3Q Max
-6.507 -1.001 0.472 0.811 2.610
Random effects:
Groups Name Variance Std.Dev. Corr
trait (Intercept) 0.0000 0.0000
subID (Intercept) 0.4779 0.6913
slope.Z 20.1334 4.4870 -0.59
predicted.Z 21.3954 4.6255 0.57 -1.00
Number of obs: 3690, groups: trait, 148; subID, 29
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.2051 0.1384 1.481 0.1385
slope.Z 1.5056 0.9093 1.656 0.0978 .
predicted.Z -1.3237 0.9392 -1.409 0.1587
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) slop.Z
slope.Z -0.573
predicted.Z 0.537 -0.994
optimizer (bobyqa) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')tidy(m,conf.int=TRUE,exponentiate=TRUE,effects="fixed")Backwards solution: Can you predict self-evaluations from similarity to ingroup and outgroup choices?
summary(m)
Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: ingChoiceN ~ eSE + (eSE | subID)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
4785.1 4816.1 -2387.5 4775.1 3635
Scaled residuals:
Min 1Q Median 3Q Max
-2.6814 -1.0139 0.5896 0.8615 2.8232
Random effects:
Groups Name Variance Std.Dev. Corr
subID (Intercept) 24.66 4.965
eSE 16.76 4.094 -1.00
Number of obs: 3640, groups: subID, 25
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.4974 1.1746 -0.424 0.672
eSE 0.6004 0.9522 0.631 0.528
Correlation of Fixed Effects:
(Intr)
eSE -0.996summary(m)
Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: ingChoiceN ~ sSE * novel + (sSE | subID) + (1 | trait)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
4791.8 4841.4 -2387.9 4775.8 3632
Scaled residuals:
Min 1Q Median 3Q Max
-2.6486 -1.0125 0.5929 0.8627 2.7918
Random effects:
Groups Name Variance Std.Dev. Corr
trait (Intercept) 0.000 0.000
subID (Intercept) 1.134 1.065
sSE 26.117 5.111 -0.90
Number of obs: 3640, groups: trait, 148; subID, 25
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.03974 0.29415 0.135 0.893
sSE 0.89300 1.28620 0.694 0.487
novel1 0.12039 0.32510 0.370 0.711
sSE:novel1 -0.36572 1.26144 -0.290 0.772
Correlation of Fixed Effects:
(Intr) sSE novel1
sSE -0.928
novel1 -0.427 0.366
sSE:novel1 0.413 -0.372 -0.976
optimizer (bobyqa) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')summary(m)
Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: as.factor(ingChoiceN) ~ scale(oSE) + (scale(oSE) | subID) + (1 | trait)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
5482.7 5520.8 -2735.3 5470.7 4215
Scaled residuals:
Min 1Q Median 3Q Max
-2.6879 -0.9939 0.5179 0.8500 2.8947
Random effects:
Groups Name Variance Std.Dev. Corr
trait (Intercept) 0.0000 0.0000
subID (Intercept) 0.3871 0.6222
scale(oSE) 0.1068 0.3267 0.59
Number of obs: 4221, groups: trait, 148; subID, 29
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.30322 0.12022 2.522 0.0117 *
scale(oSE) 0.08435 0.06924 1.218 0.2232
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
scale(oSE) 0.504
optimizer (bobyqa) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')m <- glmer( as.factor(ingChoiceN) ~ predicted.Z*scale(RSE) + ( predicted.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)boundary (singular) fit: see help('isSingular')summary(m)Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: as.factor(ingChoiceN) ~ predicted.Z * scale(RSE) + (predicted.Z | subID) + (1 | trait)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
4449.7 4498.6 -2216.8 4433.7 3364
Scaled residuals:
Min 1Q Median 3Q Max
-2.284 -1.052 0.623 0.872 2.044
Random effects:
Groups Name Variance Std.Dev. Corr
trait (Intercept) 2.669e-16 1.634e-08
subID (Intercept) 2.409e-01 4.908e-01
predicted.Z 7.502e-05 8.662e-03 -1.00
Number of obs: 3372, groups: trait, 148; subID, 25
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.24110 0.11447 2.106 0.03518 *
predicted.Z 0.34727 0.12546 2.768 0.00564 **
scale(RSE) -0.18688 0.12281 -1.522 0.12807
predicted.Z:scale(RSE) 0.11928 0.09511 1.254 0.20978
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) prdc.Z s(RSE)
predicted.Z -0.043
scale(RSE) 0.012 -0.521
prd.Z:(RSE) -0.373 0.212 -0.159
optimizer (bobyqa) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')ggpredict(m, c("predicted.Z", "RSE")) %>% plot()Data were 'prettified'. Consider using `terms="predicted.Z [all]"` to get smooth plots.
m <- glmer( as.factor(ingChoiceN) ~ predicted.Z*scale(SCC) + ( predicted.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)boundary (singular) fit: see help('isSingular')summary(m)Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: as.factor(ingChoiceN) ~ predicted.Z * scale(SCC) + (predicted.Z | subID) + (1 | trait)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
4452.1 4501.1 -2218.1 4436.1 3364
Scaled residuals:
Min 1Q Median 3Q Max
-2.2296 -1.0482 0.6315 0.8635 2.1392
Random effects:
Groups Name Variance Std.Dev. Corr
trait (Intercept) 2.572e-14 1.604e-07
subID (Intercept) 2.608e-01 5.107e-01
predicted.Z 1.120e-02 1.058e-01 -1.00
Number of obs: 3372, groups: trait, 148; subID, 25
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.297517 0.118444 2.512 0.0120 *
predicted.Z 0.279553 0.115650 2.417 0.0156 *
scale(SCC) -0.008679 0.119455 -0.073 0.9421
predicted.Z:scale(SCC) -0.046727 0.120280 -0.388 0.6977
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) prdc.Z s(SCC)
predicted.Z -0.267
scale(SCC) 0.123 -0.318
prd.Z:(SCC) -0.344 -0.080 -0.369
optimizer (bobyqa) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')ggpredict(m, c("predicted.Z", "SCC")) %>% plot()Data were 'prettified'. Consider using `terms="predicted.Z [all]"` to get smooth plots.
m <- glmer( as.factor(ingChoiceN) ~ predicted.Z*scale(DS) + ( predicted.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)boundary (singular) fit: see help('isSingular')summary(m)Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: as.factor(ingChoiceN) ~ predicted.Z * scale(DS) + (predicted.Z | subID) + (1 | trait)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
4452.3 4501.3 -2218.2 4436.3 3364
Scaled residuals:
Min 1Q Median 3Q Max
-2.2274 -1.0484 0.6330 0.8691 2.1269
Random effects:
Groups Name Variance Std.Dev. Corr
trait (Intercept) 0.000000 0.00000
subID (Intercept) 0.259573 0.50948
predicted.Z 0.009853 0.09926 -1.00
Number of obs: 3372, groups: trait, 148; subID, 25
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.285057 0.116450 2.448 0.0144 *
predicted.Z 0.258527 0.115217 2.244 0.0248 *
scale(DS) -0.020453 0.130288 -0.157 0.8753
predicted.Z:scale(DS) 0.009681 0.117406 0.082 0.9343
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) prdc.Z sc(DS)
predicted.Z -0.322
scale(DS) -0.171 0.374
prdc.Z:(DS) 0.313 -0.161 -0.584
optimizer (bobyqa) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')ggpredict(m, c("predicted.Z", "DS")) %>% plot()Data were 'prettified'. Consider using `terms="predicted.Z [all]"` to get smooth plots.
m <- glmer( as.factor(ingChoiceN) ~ predicted.Z*scale(NFC) + ( predicted.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)boundary (singular) fit: see help('isSingular')summary(m)Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: as.factor(ingChoiceN) ~ predicted.Z * scale(NFC) + (predicted.Z | subID) + (1 | trait)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
4448.3 4497.3 -2216.1 4432.3 3364
Scaled residuals:
Min 1Q Median 3Q Max
-2.2304 -1.0520 0.6456 0.8539 2.2222
Random effects:
Groups Name Variance Std.Dev. Corr
trait (Intercept) 0.000000 0.00000
subID (Intercept) 0.233963 0.48370
predicted.Z 0.008746 0.09352 -1.00
Number of obs: 3372, groups: trait, 148; subID, 25
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.31018 0.10638 2.916 0.00355 **
predicted.Z 0.31111 0.11148 2.791 0.00526 **
scale(NFC) -0.08793 0.10823 -0.812 0.41655
predicted.Z:scale(NFC) 0.26711 0.13185 2.026 0.04278 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) prdc.Z s(NFC)
predicted.Z -0.266
scale(NFC) -0.041 0.035
prd.Z:(NFC) 0.124 0.251 -0.408
optimizer (bobyqa) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')ggpredict(m, c("predicted.Z", "NFC")) %>% plot()Data were 'prettified'. Consider using `terms="predicted.Z [all]"` to get smooth plots.
m <- glmer( as.factor(ingChoiceN) ~ predicted.Z*scale(SING.Ind) + ( predicted.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)boundary (singular) fit: see help('isSingular')summary(m)Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: as.factor(ingChoiceN) ~ predicted.Z * scale(SING.Ind) + (predicted.Z | subID) + (1 | trait)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
4445.3 4494.3 -2214.7 4429.3 3364
Scaled residuals:
Min 1Q Median 3Q Max
-2.3660 -1.0464 0.6193 0.8716 1.9869
Random effects:
Groups Name Variance Std.Dev. Corr
trait (Intercept) 1.696e-15 4.118e-08
subID (Intercept) 2.129e-01 4.614e-01
predicted.Z 6.052e-03 7.779e-02 1.00
Number of obs: 3372, groups: trait, 148; subID, 25
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.22506 0.11183 2.013 0.04416 *
predicted.Z 0.49350 0.15106 3.267 0.00109 **
scale(SING.Ind) -0.41608 0.15616 -2.664 0.00771 **
predicted.Z:scale(SING.Ind) 0.12930 0.06301 2.052 0.04015 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) prdc.Z s(SING
predicted.Z 0.234
scl(SING.I) -0.124 -0.744
p.Z:(SING.I -0.391 0.062 0.074
optimizer (bobyqa) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')ggpredict(m, c("predicted.Z", "SING.Ind")) %>% plot()Data were 'prettified'. Consider using `terms="predicted.Z [all]"` to get smooth plots.
m <- glmer( as.factor(ingChoiceN) ~ predicted.Z*scale(SING.Inter) + ( predicted.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)boundary (singular) fit: see help('isSingular')summary(m)Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: as.factor(ingChoiceN) ~ predicted.Z * scale(SING.Inter) + (predicted.Z | subID) + (1 | trait)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
4450.2 4499.2 -2217.1 4434.2 3364
Scaled residuals:
Min 1Q Median 3Q Max
-2.3195 -1.0424 0.6272 0.8741 2.0284
Random effects:
Groups Name Variance Std.Dev. Corr
trait (Intercept) 6.097e-15 7.808e-08
subID (Intercept) 2.510e-01 5.010e-01
predicted.Z 1.353e-03 3.678e-02 1.00
Number of obs: 3372, groups: trait, 148; subID, 25
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.3212 0.1107 2.900 0.00373 **
predicted.Z 0.2784 0.1237 2.251 0.02437 *
scale(SING.Inter) 0.1673 0.1113 1.504 0.13262
predicted.Z:scale(SING.Inter) -0.1145 0.0868 -1.319 0.18708
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) prdc.Z s(SING
predicted.Z 0.199
scl(SING.I) 0.055 -0.040
p.Z:(SING.I -0.205 -0.382 -0.152
optimizer (bobyqa) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')ggpredict(m, c("predicted.Z", "SING.Inter")) %>% plot()Data were 'prettified'. Consider using `terms="predicted.Z [all]"` to get smooth plots.
m <- glmer( as.factor(ingChoiceN) ~ predicted.Z*scale(Proto) + ( predicted.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)boundary (singular) fit: see help('isSingular')summary(m)Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: as.factor(ingChoiceN) ~ predicted.Z * scale(Proto) + (predicted.Z | subID) + (1 | trait)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
4451.6 4500.6 -2217.8 4435.6 3364
Scaled residuals:
Min 1Q Median 3Q Max
-2.2262 -1.0496 0.6326 0.8715 2.1077
Random effects:
Groups Name Variance Std.Dev. Corr
trait (Intercept) 0.000000 0.00000
subID (Intercept) 0.246752 0.49674
predicted.Z 0.005939 0.07706 -1.00
Number of obs: 3372, groups: trait, 148; subID, 25
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.26086 0.11026 2.366 0.0180 *
predicted.Z 0.25533 0.11199 2.280 0.0226 *
scale(Proto) -0.02969 0.12083 -0.246 0.8059
predicted.Z:scale(Proto) 0.06533 0.08536 0.765 0.4441
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) prdc.Z scl(P)
predicted.Z -0.196
scale(Prot) 0.093 -0.338
prdct.Z:(P) -0.224 -0.006 -0.472
optimizer (bobyqa) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')ggpredict(m, c("predicted.Z", "Proto")) %>% plot()Data were 'prettified'. Consider using `terms="predicted.Z [all]"` to get smooth plots.
m <- glmer( as.factor(ingChoiceN) ~ predicted.Z*scale(SI) + ( predicted.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)boundary (singular) fit: see help('isSingular')summary(m)Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: as.factor(ingChoiceN) ~ predicted.Z * scale(SI) + (predicted.Z | subID) + (1 | trait)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
4451.5 4500.4 -2217.7 4435.5 3364
Scaled residuals:
Min 1Q Median 3Q Max
-2.1926 -1.0490 0.6295 0.8731 2.1288
Random effects:
Groups Name Variance Std.Dev. Corr
trait (Intercept) 1.893e-14 1.376e-07
subID (Intercept) 2.480e-01 4.980e-01
predicted.Z 1.495e-02 1.223e-01 -1.00
Number of obs: 3372, groups: trait, 148; subID, 25
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.27631 0.10918 2.531 0.0114 *
predicted.Z 0.22812 0.11042 2.066 0.0388 *
scale(SI) 0.08172 0.11025 0.741 0.4585
predicted.Z:scale(SI) 0.02415 0.08217 0.294 0.7689
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) prdc.Z sc(SI)
predicted.Z -0.293
scale(SI) 0.052 -0.128
prdc.Z:(SI) -0.101 -0.344 -0.393
optimizer (bobyqa) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')ggpredict(m, c("predicted.Z", "SI")) %>% plot()Data were 'prettified'. Consider using `terms="predicted.Z [all]"` to get smooth plots.
m <- glmer( as.factor(ingChoiceN) ~ predicted.Z*scale(NTB) + ( predicted.Z | subID) + ( predicted.Z | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)boundary (singular) fit: see help('isSingular')summary(m)Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: as.factor(ingChoiceN) ~ predicted.Z * scale(NTB) + (predicted.Z | subID) + (predicted.Z | trait)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
4450.7 4512.0 -2215.4 4430.7 3362
Scaled residuals:
Min 1Q Median 3Q Max
-2.2315 -1.0498 0.6251 0.8688 2.0892
Random effects:
Groups Name Variance Std.Dev. Corr
trait (Intercept) 1.100e-14 1.049e-07
predicted.Z 2.054e-13 4.532e-07 1.00
subID (Intercept) 2.041e-01 4.518e-01
predicted.Z 1.935e-03 4.399e-02 -1.00
Number of obs: 3372, groups: trait, 148; subID, 25
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.28922 0.09881 2.927 0.00342 **
predicted.Z 0.26267 0.10390 2.528 0.01146 *
scale(NTB) 0.23726 0.10150 2.338 0.01941 *
predicted.Z:scale(NTB) -0.05590 0.09787 -0.571 0.56790
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) prdc.Z s(NTB)
predicted.Z -0.124
scale(NTB) 0.018 -0.049
prd.Z:(NTB) 0.032 -0.353 0.177
optimizer (bobyqa) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')ggpredict(m, c("predicted.Z", "NTB")) %>% plot()Data were 'prettified'. Consider using `terms="predicted.Z [all]"` to get smooth plots.
m <- glmer( as.factor(ingChoiceN) ~ predicted.Z*scale(RSE) + ( predicted.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)boundary (singular) fit: see help('isSingular')m <- glmer( as.factor(ingChoiceN) ~ entropy.Z*scale(RSE) + ( entropy.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)boundary (singular) fit: see help('isSingular')summary(m)Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: as.factor(ingChoiceN) ~ entropy.Z * scale(RSE) + (entropy.Z | subID) + (1 | trait)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
4793.6 4843.2 -2388.8 4777.6 3632
Scaled residuals:
Min 1Q Median 3Q Max
-2.8434 -1.0083 0.5747 0.8744 2.3956
Random effects:
Groups Name Variance Std.Dev. Corr
trait (Intercept) 1.004e-18 1.002e-09
subID (Intercept) 9.301e-01 9.644e-01
entropy.Z 1.594e+00 1.262e+00 -0.55
Number of obs: 3640, groups: trait, 148; subID, 25
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.7127 0.2436 2.925 0.00344 **
entropy.Z -0.5494 0.3071 -1.789 0.07362 .
scale(RSE) -0.2484 0.2335 -1.064 0.28751
entropy.Z:scale(RSE) 0.3395 0.3118 1.089 0.27626
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) entr.Z s(RSE)
entropy.Z -0.412
scale(RSE) -0.019 0.206
ent.Z:(RSE) 0.218 -0.047 -0.388
optimizer (bobyqa) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')ggpredict(m, c("entropy.Z", "RSE")) %>% plot()Data were 'prettified'. Consider using `terms="entropy.Z [all]"` to get smooth plots.
m <- glmer( as.factor(ingChoiceN) ~ entropy.Z*scale(SCC) + ( entropy.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)boundary (singular) fit: see help('isSingular')summary(m)Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: as.factor(ingChoiceN) ~ entropy.Z * scale(SCC) + (entropy.Z | subID) + (1 | trait)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
4795.1 4844.7 -2389.6 4779.1 3632
Scaled residuals:
Min 1Q Median 3Q Max
-2.8462 -1.0069 0.5800 0.8735 2.3915
Random effects:
Groups Name Variance Std.Dev. Corr
trait (Intercept) 6.577e-20 2.564e-10
subID (Intercept) 9.521e-01 9.757e-01
entropy.Z 1.680e+00 1.296e+00 -0.56
Number of obs: 3640, groups: trait, 148; subID, 25
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.690663 0.243870 2.832 0.00462 **
entropy.Z -0.527405 0.314479 -1.677 0.09353 .
scale(SCC) -0.097378 0.240594 -0.405 0.68567
entropy.Z:scale(SCC) 0.005057 0.299650 0.017 0.98653
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) entr.Z s(SCC)
entropy.Z -0.436
scale(SCC) -0.071 0.122
ent.Z:(SCC) 0.118 0.025 -0.481
optimizer (bobyqa) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')ggpredict(m, c("entropy.Z", "SCC")) %>% plot()Data were 'prettified'. Consider using `terms="entropy.Z [all]"` to get smooth plots.
m <- glmer( as.factor(ingChoiceN) ~ entropy.Z*scale(DS) + ( entropy.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)boundary (singular) fit: see help('isSingular')summary(m)Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: as.factor(ingChoiceN) ~ entropy.Z * scale(DS) + (entropy.Z | subID) + (1 | trait)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
4793.8 4843.4 -2388.9 4777.8 3632
Scaled residuals:
Min 1Q Median 3Q Max
-2.8406 -1.0079 0.5813 0.8733 2.4261
Random effects:
Groups Name Variance Std.Dev. Corr
trait (Intercept) 0.0000 0.000
subID (Intercept) 0.8798 0.938
entropy.Z 1.6204 1.273 -0.55
Number of obs: 3640, groups: trait, 148; subID, 25
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.7324 0.2379 3.078 0.00208 **
entropy.Z -0.5417 0.3101 -1.747 0.08070 .
scale(DS) 0.2496 0.2290 1.090 0.27583
entropy.Z:scale(DS) -0.3141 0.3095 -1.015 0.31015
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) entr.Z sc(DS)
entropy.Z -0.431
scale(DS) 0.144 -0.206
entr.Z:(DS) -0.214 0.020 -0.448
optimizer (bobyqa) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')ggpredict(m, c("entropy.Z", "DS")) %>% plot()Data were 'prettified'. Consider using `terms="entropy.Z [all]"` to get smooth plots.
m <- glmer( as.factor(ingChoiceN) ~ entropy.Z*scale(NFC) + ( entropy.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)boundary (singular) fit: see help('isSingular')summary(m)Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: as.factor(ingChoiceN) ~ entropy.Z * scale(NFC) + (entropy.Z | subID) + (1 | trait)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
4791.1 4840.7 -2387.6 4775.1 3632
Scaled residuals:
Min 1Q Median 3Q Max
-2.8458 -1.0084 0.5964 0.8739 2.4604
Random effects:
Groups Name Variance Std.Dev. Corr
trait (Intercept) 0.0000 0.0000
subID (Intercept) 0.7927 0.8903
entropy.Z 1.2373 1.1124 -0.55
Number of obs: 3640, groups: trait, 148; subID, 25
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.7115 0.2278 3.123 0.00179 **
entropy.Z -0.5087 0.2797 -1.819 0.06897 .
scale(NFC) 0.2597 0.2271 1.144 0.25278
entropy.Z:scale(NFC) -0.5659 0.2716 -2.083 0.03722 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) entr.Z s(NFC)
entropy.Z -0.442
scale(NFC) 0.112 -0.175
ent.Z:(NFC) -0.190 0.048 -0.483
optimizer (bobyqa) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')ggpredict(m, c("entropy.Z", "NFC")) %>% plot()Data were 'prettified'. Consider using `terms="entropy.Z [all]"` to get smooth plots.
m <- glmer( as.factor(ingChoiceN) ~ entropy.Z*scale(SING.Ind) + ( entropy.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)boundary (singular) fit: see help('isSingular')summary(m)Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: as.factor(ingChoiceN) ~ entropy.Z * scale(SING.Ind) + (entropy.Z | subID) + (1 | trait)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
4791.7 4841.3 -2387.9 4775.7 3632
Scaled residuals:
Min 1Q Median 3Q Max
-2.8319 -1.0092 0.5749 0.8759 2.4004
Random effects:
Groups Name Variance Std.Dev. Corr
trait (Intercept) 0.0000 0.0000
subID (Intercept) 0.6746 0.8214
entropy.Z 1.4260 1.1941 -0.65
Number of obs: 3640, groups: trait, 148; subID, 25
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.6476 0.2177 2.974 0.00294 **
entropy.Z -0.6070 0.2932 -2.071 0.03840 *
scale(SING.Ind) -0.5182 0.2563 -2.022 0.04318 *
entropy.Z:scale(SING.Ind) 0.1566 0.2875 0.545 0.58605
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) entr.Z s(SING
entropy.Z -0.467
scl(SING.I) -0.005 0.244
e.Z:(SING.I 0.228 -0.066 -0.327
optimizer (bobyqa) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')ggpredict(m, c("entropy.Z", "SING.Ind")) %>% plot()Data were 'prettified'. Consider using `terms="entropy.Z [all]"` to get smooth plots.
m <- glmer( as.factor(ingChoiceN) ~ entropy.Z*scale(SING.Inter) + ( entropy.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)boundary (singular) fit: see help('isSingular')summary(m)Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: as.factor(ingChoiceN) ~ entropy.Z * scale(SING.Inter) + (entropy.Z | subID) + (1 | trait)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
4791.4 4841.0 -2387.7 4775.4 3632
Scaled residuals:
Min 1Q Median 3Q Max
-2.8785 -1.0138 0.5936 0.8820 2.4011
Random effects:
Groups Name Variance Std.Dev. Corr
trait (Intercept) 0.0000 0.0000
subID (Intercept) 0.6094 0.7806
entropy.Z 1.7311 1.3157 -0.51
Number of obs: 3640, groups: trait, 148; subID, 25
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.7522 0.2142 3.512 0.000444 ***
entropy.Z -0.4557 0.3140 -1.451 0.146682
scale(SING.Inter) 0.5286 0.2593 2.038 0.041513 *
entropy.Z:scale(SING.Inter) -0.1277 0.2946 -0.433 0.664789
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) entr.Z s(SING
entropy.Z -0.382
scl(SING.I) 0.237 -0.005
e.Z:(SING.I -0.056 0.040 -0.301
optimizer (bobyqa) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')ggpredict(m, c("entropy.Z", "SING.Inter")) %>% plot()Data were 'prettified'. Consider using `terms="entropy.Z [all]"` to get smooth plots.
m <- glmer( as.factor(ingChoiceN) ~ entropy.Z*scale(Proto) + ( entropy.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)boundary (singular) fit: see help('isSingular')summary(m)Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: as.factor(ingChoiceN) ~ entropy.Z * scale(Proto) + (entropy.Z | subID) + (1 | trait)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
4795.2 4844.8 -2389.6 4779.2 3632
Scaled residuals:
Min 1Q Median 3Q Max
-2.8447 -1.0052 0.5806 0.8732 2.3944
Random effects:
Groups Name Variance Std.Dev. Corr
trait (Intercept) 0.0000 0.0000
subID (Intercept) 0.9035 0.9505
entropy.Z 1.6478 1.2837 -0.54
Number of obs: 3640, groups: trait, 148; subID, 25
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.69010 0.23886 2.889 0.00386 **
entropy.Z -0.50042 0.31002 -1.614 0.10650
scale(Proto) 0.08185 0.23908 0.342 0.73209
entropy.Z:scale(Proto) -0.09448 0.29866 -0.316 0.75173
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) entr.Z scl(P)
entropy.Z -0.432
scale(Prot) 0.057 0.041
entrp.Z:(P) 0.011 -0.017 -0.281
optimizer (bobyqa) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')ggpredict(m, c("entropy.Z", "Proto")) %>% plot()Data were 'prettified'. Consider using `terms="entropy.Z [all]"` to get smooth plots.
m <- glmer( as.factor(ingChoiceN) ~ entropy.Z*scale(SI) + ( entropy.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)boundary (singular) fit: see help('isSingular')summary(m)Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: as.factor(ingChoiceN) ~ entropy.Z * scale(SI) + (entropy.Z | subID) + (1 | trait)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
4790.2 4839.8 -2387.1 4774.2 3632
Scaled residuals:
Min 1Q Median 3Q Max
-2.8302 -1.0044 0.5814 0.8769 2.4447
Random effects:
Groups Name Variance Std.Dev. Corr
trait (Intercept) 0.0000 0.0000
subID (Intercept) 0.6831 0.8265
entropy.Z 1.8112 1.3458 -0.64
Number of obs: 3640, groups: trait, 148; subID, 25
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.74323 0.21772 3.414 0.000641 ***
entropy.Z -0.47302 0.31535 -1.500 0.133618
scale(SI) 0.45524 0.21902 2.079 0.037661 *
entropy.Z:scale(SI) -0.01954 0.30810 -0.063 0.949443
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) entr.Z sc(SI)
entropy.Z -0.487
scale(SI) 0.165 -0.075
entr.Z:(SI) -0.024 0.071 -0.503
optimizer (bobyqa) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')ggpredict(m, c("entropy.Z", "SI")) %>% plot()Data were 'prettified'. Consider using `terms="entropy.Z [all]"` to get smooth plots.
m <- glmer( as.factor(ingChoiceN) ~ entropy.Z*scale(NTB) + ( entropy.Z | subID) + ( entropy.Z | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)boundary (singular) fit: see help('isSingular')summary(m)Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: as.factor(ingChoiceN) ~ entropy.Z * scale(NTB) + (entropy.Z | subID) + (entropy.Z | trait)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
4798.1 4860.1 -2389.0 4778.1 3630
Scaled residuals:
Min 1Q Median 3Q Max
-2.8355 -1.0062 0.5801 0.8745 2.3634
Random effects:
Groups Name Variance Std.Dev. Corr
trait (Intercept) 0.000e+00 0.000e+00
entropy.Z 5.571e-16 2.360e-08 NaN
subID (Intercept) 8.986e-01 9.479e-01
entropy.Z 1.595e+00 1.263e+00 -0.57
Number of obs: 3640, groups: trait, 148; subID, 25
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.68431 0.23774 2.878 0.0040 **
entropy.Z -0.52197 0.30393 -1.717 0.0859 .
scale(NTB) 0.24331 0.23185 1.049 0.2940
entropy.Z:scale(NTB) -0.06185 0.29468 -0.210 0.8337
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) entr.Z s(NTB)
entropy.Z -0.452
scale(NTB) 0.040 -0.082
ent.Z:(NTB) -0.066 0.031 -0.534
optimizer (bobyqa) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')ggpredict(m, c("entropy.Z", "NTB")) %>% plot()Data were 'prettified'. Consider using `terms="entropy.Z [all]"` to get smooth plots.
m <- glmer( as.factor(ingChoiceN) ~ slope.Z*scale(RSE) + ( slope.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)boundary (singular) fit: see help('isSingular')summary(m)Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: as.factor(ingChoiceN) ~ slope.Z * scale(RSE) + (slope.Z | subID) + (1 | trait)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
4717.6 4767.2 -2350.8 4701.6 3632
Scaled residuals:
Min 1Q Median 3Q Max
-4.0778 -0.9845 0.4695 0.8520 2.6235
Random effects:
Groups Name Variance Std.Dev. Corr
trait (Intercept) 0.000 0.000
subID (Intercept) 8.780 2.963
slope.Z 8.935 2.989 0.33
Number of obs: 3640, groups: trait, 148; subID, 25
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.1636 0.6400 0.256 0.7982
slope.Z 1.2559 0.6528 1.924 0.0544 .
scale(RSE) -1.1510 0.6472 -1.778 0.0753 .
slope.Z:scale(RSE) -0.6067 0.6374 -0.952 0.3412
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) slop.Z s(RSE)
slope.Z 0.284
scale(RSE) -0.004 -0.062
slp.Z:(RSE) -0.062 -0.004 0.261
optimizer (bobyqa) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')ggpredict(m, c("slope.Z", "RSE")) %>% plot()Data were 'prettified'. Consider using `terms="slope.Z [all]"` to get smooth plots.
m <- glmer( as.factor(ingChoiceN) ~ slope.Z*scale(SCC) + ( slope.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)boundary (singular) fit: see help('isSingular')summary(m)Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: as.factor(ingChoiceN) ~ slope.Z * scale(SCC) + (slope.Z | subID) + (1 | trait)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
4720.3 4769.9 -2352.2 4704.3 3632
Scaled residuals:
Min 1Q Median 3Q Max
-4.4081 -0.9850 0.4734 0.8486 2.6613
Random effects:
Groups Name Variance Std.Dev. Corr
trait (Intercept) 3.451e-16 1.858e-08
subID (Intercept) 1.003e+01 3.167e+00
slope.Z 9.640e+00 3.105e+00 0.39
Number of obs: 3640, groups: trait, 148; subID, 25
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.1138 0.6771 0.168 0.8665
slope.Z 1.2064 0.6740 1.790 0.0735 .
scale(SCC) -0.3103 0.6658 -0.466 0.6412
slope.Z:scale(SCC) 0.2471 0.6620 0.373 0.7089
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) slop.Z s(SCC)
slope.Z 0.342
scale(SCC) 0.011 -0.042
slp.Z:(SCC) -0.042 0.013 0.347
optimizer (bobyqa) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')ggpredict(m, c("slope.Z", "SCC")) %>% plot()Data were 'prettified'. Consider using `terms="slope.Z [all]"` to get smooth plots.
m <- glmer( as.factor(ingChoiceN) ~ slope.Z*scale(DS) + ( slope.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)boundary (singular) fit: see help('isSingular')summary(m)Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: as.factor(ingChoiceN) ~ slope.Z * scale(DS) + (slope.Z | subID) + (1 | trait)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
4719.6 4769.2 -2351.8 4703.6 3632
Scaled residuals:
Min 1Q Median 3Q Max
-4.2079 -0.9852 0.4703 0.8481 2.6378
Random effects:
Groups Name Variance Std.Dev. Corr
trait (Intercept) 0.000 0.000
subID (Intercept) 9.636 3.104
slope.Z 9.323 3.053 0.34
Number of obs: 3640, groups: trait, 148; subID, 25
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.1673 0.6667 0.251 0.8019
slope.Z 1.2297 0.6656 1.848 0.0647 .
scale(DS) 0.6742 0.6705 1.006 0.3146
slope.Z:scale(DS) 0.5111 0.6539 0.782 0.4344
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) slop.Z sc(DS)
slope.Z 0.299
scale(DS) 0.017 0.063
slp.Z:s(DS) 0.059 0.012 0.288
optimizer (bobyqa) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')ggpredict(m, c("slope.Z", "DS")) %>% plot()Data were 'prettified'. Consider using `terms="slope.Z [all]"` to get smooth plots.
m <- glmer( as.factor(ingChoiceN) ~ slope.Z*scale(NFC) + ( slope.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)boundary (singular) fit: see help('isSingular')summary(m)Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: as.factor(ingChoiceN) ~ slope.Z * scale(NFC) + (slope.Z | subID) + (1 | trait)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
4719.1 4768.7 -2351.5 4703.1 3632
Scaled residuals:
Min 1Q Median 3Q Max
-4.3504 -0.9885 0.4707 0.8482 2.6496
Random effects:
Groups Name Variance Std.Dev. Corr
trait (Intercept) 0.000 0.000
subID (Intercept) 9.830 3.135
slope.Z 9.302 3.050 0.41
Number of obs: 3640, groups: trait, 148; subID, 25
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.1664 0.6708 0.248 0.8041
slope.Z 1.1872 0.6637 1.789 0.0737 .
scale(NFC) -0.3811 0.6609 -0.577 0.5641
slope.Z:scale(NFC) 0.6284 0.6594 0.953 0.3406
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) slop.Z s(NFC)
slope.Z 0.357
scale(NFC) -0.017 0.010
slp.Z:(NFC) 0.011 0.027 0.344
optimizer (bobyqa) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')ggpredict(m, c("slope.Z", "NFC")) %>% plot()Data were 'prettified'. Consider using `terms="slope.Z [all]"` to get smooth plots.
m <- glmer( as.factor(ingChoiceN) ~ slope.Z*scale(SING.Ind) + ( slope.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)boundary (singular) fit: see help('isSingular')summary(m)Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: as.factor(ingChoiceN) ~ slope.Z * scale(SING.Ind) + (slope.Z | subID) + (1 | trait)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
4717.4 4767.0 -2350.7 4701.4 3632
Scaled residuals:
Min 1Q Median 3Q Max
-4.1006 -0.9855 0.4721 0.8500 2.6384
Random effects:
Groups Name Variance Std.Dev. Corr
trait (Intercept) 0.000 0.000
subID (Intercept) 8.250 2.872
slope.Z 8.988 2.998 0.36
Number of obs: 3640, groups: trait, 148; subID, 25
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.03639 0.63023 0.058 0.9540
slope.Z 1.33378 0.65833 2.026 0.0428 *
scale(SING.Ind) -1.43648 0.74774 -1.921 0.0547 .
slope.Z:scale(SING.Ind) -0.27897 0.65618 -0.425 0.6707
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) slop.Z s(SING
slope.Z 0.289
scl(SING.I) 0.103 -0.132
s.Z:(SING.I -0.124 0.028 0.091
optimizer (bobyqa) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')ggpredict(m, c("slope.Z", "SING.Ind")) %>% plot()Data were 'prettified'. Consider using `terms="slope.Z [all]"` to get smooth plots.
m <- glmer( as.factor(ingChoiceN) ~ slope.Z*scale(SING.Inter) + ( slope.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)boundary (singular) fit: see help('isSingular')summary(m)Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: as.factor(ingChoiceN) ~ slope.Z * scale(SING.Inter) + (slope.Z | subID) + (1 | trait)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
4719.2 4768.8 -2351.6 4703.2 3632
Scaled residuals:
Min 1Q Median 3Q Max
-4.3813 -0.9853 0.4706 0.8482 2.6105
Random effects:
Groups Name Variance Std.Dev. Corr
trait (Intercept) 0.000 0.000
subID (Intercept) 9.447 3.074
slope.Z 9.766 3.125 0.37
Number of obs: 3640, groups: trait, 148; subID, 25
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.23586 0.66492 0.355 0.7228
slope.Z 1.14832 0.67768 1.694 0.0902 .
scale(SING.Inter) 0.98467 0.75955 1.296 0.1948
slope.Z:scale(SING.Inter) 0.08021 0.67349 0.119 0.9052
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) slop.Z s(SING
slope.Z 0.317
scl(SING.I) 0.112 -0.034
s.Z:(SING.I -0.049 0.003 0.145
optimizer (bobyqa) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')ggpredict(m, c("slope.Z", "SING.Inter")) %>% plot()Data were 'prettified'. Consider using `terms="slope.Z [all]"` to get smooth plots.
m <- glmer( as.factor(ingChoiceN) ~ slope.Z*scale(Proto) + ( slope.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)boundary (singular) fit: see help('isSingular')summary(m)Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: as.factor(ingChoiceN) ~ slope.Z * scale(Proto) + (slope.Z | subID) + (1 | trait)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
4720.7 4770.3 -2352.4 4704.7 3632
Scaled residuals:
Min 1Q Median 3Q Max
-4.3935 -0.9845 0.4706 0.8482 2.6074
Random effects:
Groups Name Variance Std.Dev. Corr
trait (Intercept) 1.223e-15 3.497e-08
subID (Intercept) 1.005e+01 3.170e+00
slope.Z 9.616e+00 3.101e+00 0.39
Number of obs: 3640, groups: trait, 148; subID, 25
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.11611 0.67889 0.171 0.8642
slope.Z 1.19302 0.67388 1.770 0.0767 .
scale(Proto) -0.05402 0.72101 -0.075 0.9403
slope.Z:scale(Proto) 0.23967 0.66898 0.358 0.7201
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) slop.Z scl(P)
slope.Z 0.337
scale(Prot) 0.029 -0.051
slp.Z:sc(P) -0.053 0.000 0.270
optimizer (bobyqa) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')ggpredict(m, c("slope.Z", "Proto")) %>% plot()Data were 'prettified'. Consider using `terms="slope.Z [all]"` to get smooth plots.
m <- glmer( as.factor(ingChoiceN) ~ slope.Z*scale(SI) + ( slope.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)boundary (singular) fit: see help('isSingular')summary(m)Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: as.factor(ingChoiceN) ~ slope.Z * scale(SI) + (slope.Z | subID) + (1 | trait)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
4718.6 4768.2 -2351.3 4702.6 3632
Scaled residuals:
Min 1Q Median 3Q Max
-4.1024 -0.9805 0.4712 0.8492 2.6025
Random effects:
Groups Name Variance Std.Dev. Corr
trait (Intercept) 0.000 0.000
subID (Intercept) 9.008 3.001
slope.Z 9.362 3.060 0.33
Number of obs: 3640, groups: trait, 148; subID, 25
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.1666 0.6465 0.258 0.7967
slope.Z 1.1603 0.6651 1.745 0.0811 .
scale(SI) 1.0067 0.6722 1.498 0.1342
slope.Z:scale(SI) 0.5536 0.6642 0.834 0.4045
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) slop.Z sc(SI)
slope.Z 0.281
scale(SI) 0.044 -0.025
slp.Z:s(SI) -0.028 -0.006 0.273
optimizer (bobyqa) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')ggpredict(m, c("slope.Z", "SI")) %>% plot()Data were 'prettified'. Consider using `terms="slope.Z [all]"` to get smooth plots.
m <- glmer( as.factor(ingChoiceN) ~ slope.Z*scale(NTB) + ( slope.Z | subID) + ( slope.Z | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)boundary (singular) fit: see help('isSingular')summary(m)Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: as.factor(ingChoiceN) ~ slope.Z * scale(NTB) + (slope.Z | subID) + (slope.Z | trait)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
4723.3 4785.3 -2351.6 4703.3 3630
Scaled residuals:
Min 1Q Median 3Q Max
-4.4383 -0.9847 0.4726 0.8491 2.5658
Random effects:
Groups Name Variance Std.Dev. Corr
trait (Intercept) 0.000000 0.00000
slope.Z 0.001685 0.04104 NaN
subID (Intercept) 9.575622 3.09445
slope.Z 9.551674 3.09058 0.37
Number of obs: 3640, groups: trait, 148; subID, 25
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.1747 0.6654 0.263 0.7929
slope.Z 1.1860 0.6712 1.767 0.0772 .
scale(NTB) 0.8431 0.6661 1.266 0.2056
slope.Z:scale(NTB) 0.3383 0.6636 0.510 0.6102
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) slop.Z s(NTB)
slope.Z 0.327
scale(NTB) 0.045 0.003
slp.Z:(NTB) 0.004 0.012 0.376
optimizer (bobyqa) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')ggpredict(m, c("slope.Z", "NTB")) %>% plot()Data were 'prettified'. Consider using `terms="slope.Z [all]"` to get smooth plots.
m <- glmer( as.factor(ingChoiceN) ~ scale(desirability)*scale(RSE) + ( scale(desirability) | subID) + ( scale(desirability) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)boundary (singular) fit: see help('isSingular')summary(m)Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: as.factor(ingChoiceN) ~ scale(desirability) * scale(RSE) + (scale(desirability) |
subID) + (scale(desirability) | trait)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
2111.4 2165.2 -1045.7 2091.4 1594
Scaled residuals:
Min 1Q Median 3Q Max
-3.8959 -1.0144 0.4983 0.9101 1.4069
Random effects:
Groups Name Variance Std.Dev. Corr
trait (Intercept) 0.003365 0.05801
scale(desirability) 0.013385 0.11570 1.00
subID (Intercept) 0.415188 0.64435
scale(desirability) 0.001739 0.04170 1.00
Number of obs: 1604, groups: trait, 148; subID, 11
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.38520 0.20272 1.900 0.05741 .
scale(desirability) 0.20932 0.06083 3.441 0.00058 ***
scale(RSE) 0.27336 0.20314 1.346 0.17840
scale(desirability):scale(RSE) 0.02205 0.05729 0.385 0.70036
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) scl(d) s(RSE)
scl(dsrblt) 0.218
scale(RSE) 0.019 0.019
scl():(RSE) 0.018 0.128 0.233
optimizer (bobyqa) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')ggpredict(m, c("desirability", "RSE")) %>% plot()Data were 'prettified'. Consider using `terms="desirability [all]"` to get smooth plots.
m <- glmer( as.factor(ingChoiceN) ~ scale(desirability)*scale(SCC) + ( scale(desirability) | subID) + ( scale(desirability) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)boundary (singular) fit: see help('isSingular')summary(m)Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: as.factor(ingChoiceN) ~ scale(desirability) * scale(SCC) + (scale(desirability) |
subID) + (scale(desirability) | trait)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
2110.0 2163.8 -1045.0 2090.0 1594
Scaled residuals:
Min 1Q Median 3Q Max
-3.8913 -1.0078 0.4896 0.9083 1.3630
Random effects:
Groups Name Variance Std.Dev. Corr
trait (Intercept) 0.003354 0.05791
scale(desirability) 0.013479 0.11610 1.00
subID (Intercept) 0.377675 0.61455
scale(desirability) 0.003107 0.05574 1.00
Number of obs: 1604, groups: trait, 148; subID, 11
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.385673 0.194130 1.987 0.046958 *
scale(desirability) 0.209174 0.061907 3.379 0.000728 ***
scale(SCC) 0.337654 0.195663 1.726 0.084403 .
scale(desirability):scale(SCC) -0.006718 0.060638 -0.111 0.911783
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) scl(d) s(SCC)
scl(dsrblt) 0.280
scale(SCC) 0.026 0.024
scl():(SCC) 0.022 0.143 0.293
optimizer (bobyqa) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')ggpredict(m, c("desirability", "SCC")) %>% plot()Data were 'prettified'. Consider using `terms="desirability [all]"` to get smooth plots.
m <- glmer( as.factor(ingChoiceN) ~ scale(desirability)*scale(DS) + ( scale(desirability) | subID) + ( scale(desirability) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)boundary (singular) fit: see help('isSingular')summary(m)Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: as.factor(ingChoiceN) ~ scale(desirability) * scale(DS) + (scale(desirability) |
subID) + (scale(desirability) | trait)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
2110.9 2164.7 -1045.4 2090.9 1594
Scaled residuals:
Min 1Q Median 3Q Max
-3.7639 -1.0088 0.4900 0.9098 1.4151
Random effects:
Groups Name Variance Std.Dev. Corr
trait (Intercept) 0.0034089 0.05839
scale(desirability) 0.0134907 0.11615 1.00
subID (Intercept) 0.3893895 0.62401
scale(desirability) 0.0009413 0.03068 1.00
Number of obs: 1604, groups: trait, 148; subID, 11
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.38260 0.19680 1.944 0.051891 .
scale(desirability) 0.20787 0.06023 3.451 0.000558 ***
scale(DS) -0.29740 0.19610 -1.517 0.129382
scale(desirability):scale(DS) -0.03279 0.05445 -0.602 0.547033
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) scl(d) sc(DS)
scl(dsrblt) 0.167
scale(DS) -0.015 -0.017
scl(d):(DS) -0.015 -0.103 0.179
optimizer (bobyqa) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')ggpredict(m, c("desirability", "DS")) %>% plot()Data were 'prettified'. Consider using `terms="desirability [all]"` to get smooth plots.
m <- glmer( as.factor(ingChoiceN) ~ scale(desirability)*scale(NFC) + ( scale(desirability) | subID) + ( scale(desirability) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)boundary (singular) fit: see help('isSingular')summary(m)Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: as.factor(ingChoiceN) ~ scale(desirability) * scale(NFC) + (scale(desirability) |
subID) + (scale(desirability) | trait)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
2112.6 2166.4 -1046.3 2092.6 1594
Scaled residuals:
Min 1Q Median 3Q Max
-3.8577 -1.0118 0.4889 0.9094 1.3512
Random effects:
Groups Name Variance Std.Dev. Corr
trait (Intercept) 0.003355 0.05792
scale(desirability) 0.013450 0.11598 1.00
subID (Intercept) 0.479426 0.69241
scale(desirability) 0.002268 0.04762 1.00
Number of obs: 1604, groups: trait, 148; subID, 11
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.38517 0.21664 1.778 0.075415 .
scale(desirability) 0.20871 0.06126 3.407 0.000657 ***
scale(NFC) -0.09903 0.21536 -0.460 0.645636
scale(desirability):scale(NFC) 0.01942 0.05410 0.359 0.719619
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) scl(d) s(NFC)
scl(dsrblt) 0.244
scale(NFC) -0.008 -0.007
scl():(NFC) -0.008 -0.060 0.264
optimizer (bobyqa) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')ggpredict(m, c("desirability", "NFC")) %>% plot()Data were 'prettified'. Consider using `terms="desirability [all]"` to get smooth plots.
m <- glmer( as.factor(ingChoiceN) ~ scale(desirability)*scale(SING.Ind) + ( scale(desirability) | subID) + ( scale(desirability) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)boundary (singular) fit: see help('isSingular')summary(m)Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: as.factor(ingChoiceN) ~ scale(desirability) * scale(SING.Ind) +
(scale(desirability) | subID) + (scale(desirability) | trait)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
2112.7 2166.5 -1046.4 2092.7 1594
Scaled residuals:
Min 1Q Median 3Q Max
-3.8602 -1.0147 0.4910 0.9059 1.3673
Random effects:
Groups Name Variance Std.Dev. Corr
trait (Intercept) 0.003337 0.05777
scale(desirability) 0.013431 0.11589 1.00
subID (Intercept) 0.486231 0.69730
scale(desirability) 0.002218 0.04710 1.00
Number of obs: 1604, groups: trait, 148; subID, 11
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.38508 0.21806 1.766 0.077406 .
scale(desirability) 0.20921 0.06118 3.419 0.000628 ***
scale(SING.Ind) 0.05346 0.21663 0.247 0.805083
scale(desirability):scale(SING.Ind) -0.02489 0.05367 -0.464 0.642820
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) scl(d) s(SING
scl(dsrblt) 0.242
scl(SING.I) 0.004 0.003
s():(SING.I 0.003 0.022 0.263
optimizer (bobyqa) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')ggpredict(m, c("desirability", "SING.Ind")) %>% plot()Data were 'prettified'. Consider using `terms="desirability [all]"` to get smooth plots.
m <- glmer( as.factor(ingChoiceN) ~ scale(desirability)*scale(SING.Inter) + ( scale(desirability) | subID) + ( scale(desirability) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)boundary (singular) fit: see help('isSingular')summary(m)Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: as.factor(ingChoiceN) ~ scale(desirability) * scale(SING.Inter) +
(scale(desirability) | subID) + (scale(desirability) | trait)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
2110.4 2164.2 -1045.2 2090.4 1594
Scaled residuals:
Min 1Q Median 3Q Max
-3.8256 -1.0104 0.4934 0.9125 1.4150
Random effects:
Groups Name Variance Std.Dev. Corr
trait (Intercept) 0.0000000 0.00000
scale(desirability) 0.0093185 0.09653 NaN
subID (Intercept) 0.3727206 0.61051
scale(desirability) 0.0009385 0.03063 1.00
Number of obs: 1604, groups: trait, 148; subID, 11
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.38345 0.19280 1.989 0.046716 *
scale(desirability) 0.20642 0.05956 3.466 0.000529 ***
scale(SING.Inter) -0.33236 0.19353 -1.717 0.085910 .
scale(desirability):scale(SING.Inter) -0.03516 0.05701 -0.617 0.537466
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) scl(d) s(SING
scl(dsrblt) 0.162
scl(SING.I) -0.022 -0.021
s():(SING.I -0.020 -0.138 0.177
optimizer (bobyqa) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')ggpredict(m, c("desirability", "SING.Inter")) %>% plot()Data were 'prettified'. Consider using `terms="desirability [all]"` to get smooth plots.
m <- glmer( as.factor(ingChoiceN) ~ scale(desirability)*scale(Proto) + ( scale(desirability) | subID) + ( scale(desirability) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)boundary (singular) fit: see help('isSingular')summary(m)Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: as.factor(ingChoiceN) ~ scale(desirability) * scale(Proto) +
(scale(desirability) | subID) + (scale(desirability) | trait)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
2109.2 2163.0 -1044.6 2089.2 1594
Scaled residuals:
Min 1Q Median 3Q Max
-4.3834 -1.0152 0.5623 0.9062 1.4029
Random effects:
Groups Name Variance Std.Dev. Corr
trait (Intercept) 0.003279 0.05726
scale(desirability) 0.013172 0.11477 1.00
subID (Intercept) 0.374479 0.61195
scale(desirability) 0.001273 0.03567 1.00
Number of obs: 1604, groups: trait, 148; subID, 11
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.39167 0.19357 2.023 0.043030 *
scale(desirability) 0.21533 0.06089 3.536 0.000406 ***
scale(Proto) -0.38362 0.20188 -1.900 0.057402 .
scale(desirability):scale(Proto) -0.07237 0.07165 -1.010 0.312464
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) scl(d) scl(P)
scl(dsrblt) 0.194
scale(Prot) -0.043 -0.047
scl(ds):(P) -0.038 -0.225 0.229
optimizer (bobyqa) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')ggpredict(m, c("desirability", "Proto")) %>% plot()Data were 'prettified'. Consider using `terms="desirability [all]"` to get smooth plots.
m <- glmer( as.factor(ingChoiceN) ~ scale(desirability)*scale(SI) + ( scale(desirability) | subID) + ( scale(desirability) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)boundary (singular) fit: see help('isSingular')summary(m)Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: as.factor(ingChoiceN) ~ scale(desirability) * scale(SI) + (scale(desirability) |
subID) + (scale(desirability) | trait)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
2110.7 2164.5 -1045.4 2090.7 1594
Scaled residuals:
Min 1Q Median 3Q Max
-4.0641 -1.0169 0.5313 0.9074 1.5565
Random effects:
Groups Name Variance Std.Dev. Corr
trait (Intercept) 0.003313 0.05756
scale(desirability) 0.013506 0.11621 1.00
subID (Intercept) 0.492358 0.70168
scale(desirability) 0.002593 0.05092 1.00
Number of obs: 1604, groups: trait, 148; subID, 11
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.38629 0.21938 1.761 0.078272 .
scale(desirability) 0.21359 0.06156 3.470 0.000521 ***
scale(SI) -0.05538 0.21816 -0.254 0.799632
scale(desirability):scale(SI) -0.08211 0.05464 -1.503 0.132865
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) scl(d) sc(SI)
scl(dsrblt) 0.260
scale(SI) -0.008 -0.009
scl(d):(SI) -0.009 -0.079 0.280
optimizer (bobyqa) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')ggpredict(m, c("desirability", "SI")) %>% plot()Data were 'prettified'. Consider using `terms="desirability [all]"` to get smooth plots.
m <- glmer( as.factor(ingChoiceN) ~ scale(desirability)*scale(NTB) + ( scale(desirability) | subID) + ( scale(desirability) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)boundary (singular) fit: see help('isSingular')summary(m)Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: as.factor(ingChoiceN) ~ scale(desirability) * scale(NTB) + (scale(desirability) |
subID) + (scale(desirability) | trait)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
2112.8 2166.6 -1046.4 2092.8 1594
Scaled residuals:
Min 1Q Median 3Q Max
-3.9039 -1.0131 0.5005 0.9074 1.4211
Random effects:
Groups Name Variance Std.Dev. Corr
trait (Intercept) 0.00337 0.05805
scale(desirability) 0.01338 0.11569 1.00
subID (Intercept) 0.47789 0.69130
scale(desirability) 0.00211 0.04593 1.00
Number of obs: 1604, groups: trait, 148; subID, 11
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.38539 0.21633 1.782 0.07483 .
scale(desirability) 0.20947 0.06112 3.427 0.00061 ***
scale(NTB) -0.11353 0.21542 -0.527 0.59819
scale(desirability):scale(NTB) -0.01384 0.05480 -0.253 0.80060
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) scl(d) s(NTB)
scl(dsrblt) 0.237
scale(NTB) -0.010 -0.010
scl():(NTB) -0.009 -0.075 0.255
optimizer (bobyqa) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')ggpredict(m, c("desirability", "NTB")) %>% plot()Data were 'prettified'. Consider using `terms="desirability [all]"` to get smooth plots.
summary(m)
Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: as.factor(ingChoiceN) ~ predicted.Z * novel * scale(NFC) + (predicted.Z +
novel | subID) + (SE.Z * as.factor(novel) | trait)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
4476.7 4623.6 -2214.3 4428.7 3348
Scaled residuals:
Min 1Q Median 3Q Max
-2.2596 -1.0499 0.6322 0.8416 2.2666
Random effects:
Groups Name Variance Std.Dev. Corr
trait (Intercept) 0.000000 0.00000
SE.Z 0.001061 0.03257 NaN
as.factor(novel)Held Out 0.002307 0.04803 NaN -1.00
SE.Z:as.factor(novel)Held Out 0.038538 0.19631 NaN -1.00 1.00
subID (Intercept) 0.257661 0.50760
predicted.Z 0.008917 0.09443 -1.00
novelHeld Out 0.002718 0.05214 -1.00 1.00
Number of obs: 3372, groups: trait, 148; subID, 25
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.298628 0.116464 2.564 0.01034 *
predicted.Z 0.318133 0.123109 2.584 0.00976 **
novelHeld Out 0.031633 0.078956 0.401 0.68869
scale(NFC) -0.101568 0.117287 -0.866 0.38650
predicted.Z:novelHeld Out -0.007964 0.083210 -0.096 0.92376
predicted.Z:scale(NFC) 0.225593 0.143761 1.569 0.11660
novelHeld Out:scale(NFC) 0.037730 0.075930 0.497 0.61925
predicted.Z:novelHeld Out:scale(NFC) 0.108722 0.096644 1.125 0.26060
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) prdc.Z nvlHlO s(NFC) pr.Z:HO p.Z:(N nHO:(N
predicted.Z -0.260
novelHeldOt -0.409 0.048
scale(NFC) -0.018 0.017 -0.036
prdctd.Z:HO 0.057 -0.424 -0.001 0.025
prd.Z:(NFC) 0.097 0.270 -0.001 -0.400 -0.138
nvlHO:(NFC) -0.040 0.027 0.084 -0.393 -0.037 0.129
p.Z:HO:(NFC 0.003 -0.139 0.046 0.124 0.312 -0.405 -0.259
optimizer (bobyqa) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')m <- glmer( as.factor(ingChoiceN) ~ SE.Z*as.factor(novel)*scale(SCC) + ( SE.Z+as.factor(novel) | subID) + ( SE.Z+as.factor(novel) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)
summary(m)
ggpredict(m, c("SE","novel","SCC")) %>% plot()m <- glmer( as.factor(ingChoiceN) ~ SE.Z*as.factor(novel)*scale(DS) + ( SE.Z+as.factor(novel) | subID) + ( SE.Z+as.factor(novel) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)
summary(m)
ggpredict(m, c("SE","novel","DS")) %>% plot()m <- glmer( as.factor(ingChoiceN) ~ SE.Z*as.factor(novel)*scale(NFC) + ( SE.Z+as.factor(novel) | subID) + ( SE.Z+as.factor(novel) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)
summary(m)
ggpredict(m, c("SE","novel", "NFC")) %>% plot()m <- glmer( as.factor(ingChoiceN) ~ SE.Z*as.factor(novel)*scale(SING.Ind) + ( SE.Z+as.factor(novel) | subID) + ( SE.Z+as.factor(novel) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)
summary(m)
ggpredict(m, c("SE","novel","SING.Ind")) %>% plot()m <- glmer( as.factor(ingChoiceN) ~ SE.Z*as.factor(novel)*scale(SING.Inter) + ( SE.Z+as.factor(novel) | subID) + ( SE.Z+as.factor(novel) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)
summary(m)
ggpredict(m, c("SE","novel","SING.Inter")) %>% plot()m <- glmer( as.factor(ingChoiceN) ~ SE.Z*as.factor(novel)*scale(Proto) + ( SE.Z+as.factor(novel) | subID) + ( SE.Z+as.factor(novel) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)
summary(m)
ggpredict(m, c("SE", "Proto")) %>% plot()m <- glmer( as.factor(ingChoiceN) ~ SE.Z*as.factor(novel)*scale(SI) + ( SE.Z+as.factor(novel) | subID) + ( SE.Z+as.factor(novel) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)
summary(m)
ggpredict(m, c("SE","novel","SI")) %>% plot()m <- glmer( as.factor(ingChoiceN) ~ SE.Z*as.factor(novel)*scale(NTB) + ( SE.Z+as.factor(novel) | subID) + ( SE.Z+as.factor(novel) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)
summary(m)
ggpredict(m, c("SE", "novel", "NTB")) %>% plot()---
title: "R Notebook"
output: html_notebook
---

```{r}
library(lmerTest)
library(ggeffects)
library(dplyr)
library(report)
library(r2glmm)
library(performance)
library(broom.mixed)
```

```{r}
fullTest <- read.csv("../Cleaning/output/fullTest.csv")
fullTrain <- read.csv("../Cleaning/output/fullTrain.csv")

traitsFreqs <- read.csv("../Cleaning/output/traitFreqOverUnder.csv")
traitsFreqs <- rename(traitsFreqs, props = optionChoiceN)
fullTest <- merge(fullTest, traitsFreqs[c("trait","props")], by = "trait")
fullTest$propCorr <- ifelse(fullTest$Estimator=="Underestimator", 1-fullTest$props, fullTest$props)

uSubs <- unique(fullTest$subID)

indDiffs <- fullTest[!duplicated(fullTest$subID),]
```

```{r}
fullTest$ingChoiceN <- as.factor(fullTest$ingChoiceN)
fullTest$novel <- as.factor(fullTest$novel)
fullTest$selfResp.Z <- scale(fullTest$selfResp)
fullTest$SE.Z <- scale(fullTest$SE)
fullTest$iSE.Z <- scale(fullTest$iSE)
fullTest$oSE.Z <- scale(fullTest$oSE)
fullTest$predicted.Z <- scale(fullTest$predicted)
fullTest$slope.Z <- scale(fullTest$slope)
fullTest$entropy.Z <- scale(fullTest$entropy)
fullTest$WSR.Z <- scale(fullTest$WSR)
fullTest$neighAveOutSE.Z <- scale(fullTest$neighAveOutSE)
fullTest$neighAveAllSE.Z <- scale(fullTest$neighAveAllSE)
fullTest$neighAveInSE.Z <- scale(fullTest$neighAveInSE)
```


```{r}
fullTest$novel <- as.factor(fullTest$novel)
levels(fullTest$novel) <- list("Trained"  = "0", "Held Out" = "1")
```


```{r}
# prop.test(traitsFreqs$optionChoiceN, traitsFreqs$N, p=rep(.5,length(traitsFreqs$N)))
# 
# m <- glmer( ingChoiceN ~ trait + ( 1 | subID) , data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
#                                     optCtrl = list(maxfun = 100000)),
#     nAGQ = 1)
# 
# fullTest$trait <- as.factor(fullTest$trait)
# contrasts(fullTest$trait) <- contr.sum(148)
# m <- glm(optionChoiceN ~ trait, family = binomial,
#           data = fullTest
#          )
# summary(m)
```


```{r}
# traitsFreqs$trait <- as.factor(traitsFreqs$trait)
# contrasts(traitsFreqs$trait) <- contr.sum(148)
# m <- glm(optionChoiceN ~ 1, family = binomial,
#           data = traitsFreqs
#          )
# summary(m)
# check_overdispersion(m)
# check_model(m)
# 
# m <- glm(optionChoiceN ~ trait, family = quasibinomial,
#           data = traitsFreqs
#          )
# check_overdispersion(m)
# check_model(m)
# 
# t.test()
# 
# m <- glm(optionChoiceN ~ trait, family = poisson,
#           data = traitsFreqs
#          )
# check_overdispersion(m)
# check_model(m)
```

```{r}
propMatrix <- matrix(nrow=148,ncol=7)
for(i in 1:148){
    traitDf <- subset(fullTest, Idx==i)
    test <- t.test(as.numeric(traitDf$ingChoiceN)-1, mu=.50)
    propMatrix[i, ] <- c(i, test$statistic, test$p.value, test$conf.int, test$estimate, test$parameter)
}
colnames(propMatrix) <- c("Idx", "stat", "p", "LCI", "UCI", "est", "param")
propMatrix <- as.data.frame(propMatrix)
propMatrix$trait <- traitsFreqs$trait
propMatrix <- propMatrix[order(propMatrix$p),]
```

```{r}
library(corrr)
x <- indDiffs %>% 
    select(groupHomoph, DS:SING.Inter) %>%
  correlate() %>% 
  focus(groupHomoph) %>%
    arrange(groupHomoph)

x %>% 
  mutate(rowname = factor(rowname, levels = rowname[order(groupHomoph)])) %>%  # Order by correlation strength
  ggplot(aes(x = rowname, y = groupHomoph)) +
    geom_bar(stat = "identity") +
    ylab("Correlation Coefficient") +
    xlab("Individual Differences") + theme_grey(base_size = 9)  + theme(axis.text.x = element_text(angle = 90,hjust = 1)) +
  theme(axis.text.x = element_text( 
                           size = 9, angle = 45, vjust = 1)) + theme(axis.title.x = element_text(vjust=1.9)) + theme(axis.text=element_text(size=9),
        axis.title=element_text(size=9,face="bold")) + theme(legend.text = element_text(size=9)) + theme(panel.border = element_rect(colour = "black", fill = NA, size =1)) + theme(legend.title = element_blank()) + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.line = element_line(colour = "black"))
```
# Familiarity predicts Reaction Time

```{r}
m <- lmer( log(RT) ~ fam + ( fam | subID) + ( 1 | trait), data = fullTest)
summary(m)
ggpredict(m, c("fam")) %>% plot()
tidy(m,conf.int=TRUE,exponentiate=TRUE,effects="fixed")
```

```{r}
m <- lmer( log(RT) ~ fam + propCorr + desirability + ( fam | subID) + ( 1 | trait), data = fullTest)
summary(m)
ggpredict(m, c("fam")) %>% plot()
tidy(m,conf.int=TRUE,exponentiate=TRUE,effects="fixed")
```


# Replication of prior self-anchoring findings: Self-evaluations predicting ingroup evaluations

```{r}
m <- glmer( ingChoiceN ~ selfResp.Z + propCorr + desirability + ( selfResp.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
library(broom.mixed)
tidy(m,conf.int=TRUE,exponentiate=TRUE,effects="fixed")
r2beta(m)
ggpredict(m, c("selfResp.Z")) %>% plot(show.title=F) + xlab("Self-Descriptiveness") + ylab("Likelihood of Ingroup Choice") + jtools::theme_apa()
ggsave("~/Documents/UC Riverside/Studies/Self-Anchoring/Figures/SelfProjection.tiff",dpi=600)
```

# Does similarity-weighted self-evaluation average predict ingroup choices?

```{r}
m <- glmer( ingChoiceN ~ WSR.Z + propCorr + desirability + ( WSR.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
tidy(m,conf.int=TRUE,exponentiate=TRUE,effects="fixed")
r2beta(m)
```
# Does self-evaluation weighted similarity predict ingroup choices?

```{r}
m <- glmer( ingChoiceN ~ SE.Z + propCorr + desirability + ( SE.Z | subID) , data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
tidy(m,conf.int=TRUE,exponentiate=TRUE,effects="fixed")
r2beta(m)
```

# Do cross-validated similarity*self-evaluation predictions predict ingroup choices? 

```{r}
m <- glmer( ingChoiceN ~ predicted.Z + propCorr + desirability + ( predicted.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
tidy(m,conf.int=TRUE,exponentiate=TRUE,effects="fixed")
r2beta(m)
ggpredict(m, c("predicted.Z")) %>% plot(show.title=F) + xlab("Cross-Validated Self-Descriptiveness Predictions") + ylab("Likelihood of Ingroup Choice") + jtools::theme_apa()
ggsave("~/Documents/UC Riverside/Studies/Self-Anchoring/Figures/CVprediction.tiff",dpi=600)
```

# Do cross-validated similarity*self-evaluation predictions predict ingroup choices, regardless of whether it was seen prior or not?

```{r}
m <- glmer( ingChoiceN ~ predicted.Z * novel + ( predicted.Z + novel | subID) , data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
tidy(m,conf.int=TRUE,exponentiate=TRUE,effects="fixed")
r2beta(m)
ggpredict(m, c("predicted.Z", "novel")) %>% plot(show.title=F)+ xlab("Cross-Validated Self-Descriptiveness") + ylab("Likelihood of Ingroup Choice") + jtools::theme_apa()  + scale_color_discrete(labels = c("Trained","Held-Out"))
ggsave("~/Documents/UC Riverside/Studies/Self-Anchoring/Figures/CVpredictionGeneralize.tiff",dpi=600)
```

# Does generalization depend on outdegree?

```{r}
m <- glmer( ingChoiceN ~ predicted.Z * novel * outDegree  + ( predicted.Z + novel | subID) , data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
tidy(m,conf.int=TRUE,exponentiate=TRUE,effects="fixed")
r2beta(m)
ggpredict(m, c("predicted.Z", "outDegree" ,"novel")) %>% plot(show.title=F)+ xlab("Cross-Validated Self-Descriptiveness") + ylab("Likelihood of Ingroup Choice") + jtools::theme_apa()
ggsave("~/Documents/UC Riverside/Studies/Self-Anchoring/Figures/CVpredictionGeneralize.tiff",dpi=600)
```
# Does generalization depend on indegree?

Nope

```{r}
m <- glmer( ingChoiceN ~ predicted.Z * novel * inDegree + ( predicted.Z + novel | subID) , data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
tidy(m,conf.int=TRUE,exponentiate=TRUE,effects="fixed")
r2beta(m)
ggpredict(m, c("predicted.Z", "outDegree" ,"novel")) %>% plot(show.title=F)+ xlab("Cross-Validated Self-Descriptiveness") + ylab("Likelihood of Ingroup Choice") + jtools::theme_apa()
#ggsave("~/Documents/UC Riverside/Studies/Self-Anchoring/Figures/CVpredictionGeneralize.tiff",dpi=600)
```

# Neighboring Dependencies Predicting Choices

```{r}
m <- glmer( ingChoiceN ~ neighAveOutSE.Z  + propCorr + desirability + ( neighAveOutSE.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
tidy(m,conf.int=TRUE,exponentiate=TRUE,effects="fixed")
r2beta(m)
ggpredict(m, c("neighAveOutSE.Z")) %>% plot(show.title=F)+ xlab("Outwards Neighboring Self-Evaluations") + ylab("Likelihood of Ingroup Choice") + jtools::theme_apa()
ggsave("~/Documents/UC Riverside/Studies/Self-Anchoring/Figures/outdegreeNeighbors.tiff",dpi=600)
```

# Generalization of Outdegree Neighboring Self-Evaluations

```{r}
m <- glmer( ingChoiceN ~ neighAveOutSE.Z * novel + ( neighAveOutSE.Z + novel | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
tidy(m,conf.int=TRUE,exponentiate=TRUE,effects="fixed")
r2beta(m)
ggpredict(m, c("neighAveOutSE.Z","novel")) %>% plot(show.title=F)+ xlab("Outwards Neighboring Self-Evaluations") + ylab("Likelihood of Ingroup Choice") + jtools::theme_apa()
ggsave("~/Documents/UC Riverside/Studies/Self-Anchoring/Figures/outdegreeNeighborsGeneralization.tiff",dpi=600)
```

# Does entropy (i.e., uncertainty) predict likelihood of ingroup choices?

```{r}
m <- glmer( ingChoiceN ~ entropy.Z  + propCorr + desirability + ( entropy.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
tidy(m,conf.int=TRUE,exponentiate=TRUE,effects="fixed")
r2beta(m)
ggpredict(m, c("entropy.Z")) %>% plot(show.title=F) + xlab("Uncertainty") + ylab("Likelihood of Ingroup Choice") + jtools::theme_apa()
ggsave("~/Documents/UC Riverside/Studies/Self-Anchoring/Figures/Uncertainty.tiff",dpi=600)
```

# Does a linear trend of similarity-based probabilities predict ingroup choices?

```{r}
m <- glmer( ingChoiceN ~ slope.Z  + propCorr + desirability + ( slope.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
tidy(m,conf.int=TRUE,exponentiate=TRUE,effects="fixed")
ggpredict(m, c("slope.Z")) %>% plot(show.title=F) + xlab("Linear Trend of Greater Self-Descriptiveness") + ylab("Likelihood of Ingroup Choice") + jtools::theme_apa()
ggsave("~/Documents/UC Riverside/Studies/Self-Anchoring/Figures/Slope.tiff",dpi=600)
```

# Does a linear trend of similarity-based probabilities predict ingroup choices, controlling for self-descriptiveness?

```{r}
m <- glmer( ingChoiceN ~ scale(slope) + selfResp.Z  + ( scale(slope) + selfResp.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
tidy(m,conf.int=TRUE,exponentiate=TRUE,effects="fixed")
```

# Does a non-parametric trend of similarity-based probabilities predict ingroup choices, controlling for self-descriptiveness?

```{r}
m <- glmer( ingChoiceN ~ scale(nlslope) + selfResp.Z +  ( scale(nlslope) + selfResp.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
tidy(m,conf.int=TRUE,exponentiate=TRUE,effects="fixed")
```

# Does a linear trend of similarity-based probabilities predict ingroup choices?

```{r}
m <- glmer( ingChoiceN ~ slope.Z  + predicted.Z + ( slope.Z + predicted.Z | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
tidy(m,conf.int=TRUE,exponentiate=TRUE,effects="fixed")
```

# Backwards solution: Can you predict self-evaluations from similarity to ingroup and outgroup choices?

```{r}
m <- lmer( scale(selfResp) ~ scale(inGsim) + scale(outGsim) + (  scale(inGsim) + scale(outGsim) | subID) + (1 | trait), data = fullTrain)
summary(m)
tidy(m,conf.int=TRUE,effects="fixed")
ggpredict(m, c("inGsim")) %>% plot(show.title=F) + xlab("Similarity to Ingroup Choices") + ylab("Self-Evaluation") + jtools::theme_apa()
```

```{r}
m <- glmer( ingChoiceN ~ scale(slope) * novel  + ( scale(slope) + novel | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
tidy(m,conf.int=TRUE,exponentiate=TRUE,effects="fixed")
```

```{r}
m <- glmer( ingChoiceN ~ eSE + ( eSE | subID) , data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
```


```{r}
m <- glmer( ingChoiceN ~ sSE + ( sSE | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
tidy(m,conf.int=TRUE,exponentiate=TRUE,effects="fixed")
r2beta(m)
```

```{r}
m <- glmer( ingChoiceN ~ SE.Z * novel + ( SE.Z + novel | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
tidy(m,conf.int=TRUE,exponentiate=TRUE,effects="fixed")
r2beta(m)
```


```{r}
m <- glmer( as.factor(ingChoiceN) ~ SE.Z + scale(desirability) + ( SE.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)

m <- glmer( as.factor(ingChoiceN) ~ scale(oSE) + ( scale(oSE) | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)

m <- glmer( as.factor(ingChoiceN) ~ scale(iSE) + ( scale(iSE) | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)

m <- glmer( as.factor(ingChoiceN) ~ SE.Z*as.factor(novel) + ( SE.Z + as.factor(novel) | subID) + (  1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)

m <- glmer( as.factor(ingChoiceN) ~ SE.Z*as.factor(novel) + scale(desirability) + ( SE.Z + as.factor(novel) + scale(desirability) | subID) + (  1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("SE", "novel")) %>% plot()
```

```{r}
m <- glmer( as.factor(ingChoiceN) ~ predicted.Z*scale(RSE) + ( predicted.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("predicted.Z", "RSE")) %>% plot()

m <- glmer( as.factor(ingChoiceN) ~ predicted.Z*scale(SCC) + ( predicted.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("predicted.Z", "SCC")) %>% plot()

m <- glmer( as.factor(ingChoiceN) ~ predicted.Z*scale(DS) + ( predicted.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("predicted.Z", "DS")) %>% plot()

m <- glmer( as.factor(ingChoiceN) ~ predicted.Z*scale(NFC) + ( predicted.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("predicted.Z", "NFC")) %>% plot()

m <- glmer( as.factor(ingChoiceN) ~ predicted.Z*scale(SING.Ind) + ( predicted.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("predicted.Z", "SING.Ind")) %>% plot()

m <- glmer( as.factor(ingChoiceN) ~ predicted.Z*scale(SING.Inter) + ( predicted.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("predicted.Z", "SING.Inter")) %>% plot()

m <- glmer( as.factor(ingChoiceN) ~ predicted.Z*scale(Proto) + ( predicted.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("predicted.Z", "Proto")) %>% plot()

m <- glmer( as.factor(ingChoiceN) ~ predicted.Z*scale(SI) + ( predicted.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("predicted.Z", "SI")) %>% plot()

m <- glmer( as.factor(ingChoiceN) ~ predicted.Z*scale(NTB) + ( predicted.Z | subID) + ( predicted.Z | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("predicted.Z", "NTB")) %>% plot()
```

```{r}
m <- glmer( as.factor(ingChoiceN) ~ entropy.Z*scale(RSE) + ( entropy.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("entropy.Z", "RSE")) %>% plot()

m <- glmer( as.factor(ingChoiceN) ~ entropy.Z*scale(SCC) + ( entropy.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("entropy.Z", "SCC")) %>% plot()

m <- glmer( as.factor(ingChoiceN) ~ entropy.Z*scale(DS) + ( entropy.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("entropy.Z", "DS")) %>% plot()

m <- glmer( as.factor(ingChoiceN) ~ entropy.Z*scale(NFC) + ( entropy.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("entropy.Z", "NFC")) %>% plot()

m <- glmer( as.factor(ingChoiceN) ~ entropy.Z*scale(SING.Ind) + ( entropy.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("entropy.Z", "SING.Ind")) %>% plot()

m <- glmer( as.factor(ingChoiceN) ~ entropy.Z*scale(SING.Inter) + ( entropy.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("entropy.Z", "SING.Inter")) %>% plot()

m <- glmer( as.factor(ingChoiceN) ~ entropy.Z*scale(Proto) + ( entropy.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("entropy.Z", "Proto")) %>% plot()

m <- glmer( as.factor(ingChoiceN) ~ entropy.Z*scale(SI) + ( entropy.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("entropy.Z", "SI")) %>% plot()

m <- glmer( as.factor(ingChoiceN) ~ entropy.Z*scale(NTB) + ( entropy.Z | subID) + ( entropy.Z | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("entropy.Z", "NTB")) %>% plot()
```

```{r}
m <- glmer( as.factor(ingChoiceN) ~ slope.Z*scale(RSE) + ( slope.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("slope.Z", "RSE")) %>% plot()

m <- glmer( as.factor(ingChoiceN) ~ slope.Z*scale(SCC) + ( slope.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("slope.Z", "SCC")) %>% plot()

m <- glmer( as.factor(ingChoiceN) ~ slope.Z*scale(DS) + ( slope.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("slope.Z", "DS")) %>% plot()

m <- glmer( as.factor(ingChoiceN) ~ slope.Z*scale(NFC) + ( slope.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("slope.Z", "NFC")) %>% plot()

m <- glmer( as.factor(ingChoiceN) ~ slope.Z*scale(SING.Ind) + ( slope.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("slope.Z", "SING.Ind")) %>% plot()

m <- glmer( as.factor(ingChoiceN) ~ slope.Z*scale(SING.Inter) + ( slope.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("slope.Z", "SING.Inter")) %>% plot()

m <- glmer( as.factor(ingChoiceN) ~ slope.Z*scale(Proto) + ( slope.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("slope.Z", "Proto")) %>% plot()

m <- glmer( as.factor(ingChoiceN) ~ slope.Z*scale(SI) + ( slope.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("slope.Z", "SI")) %>% plot()

m <- glmer( as.factor(ingChoiceN) ~ slope.Z*scale(NTB) + ( slope.Z | subID) + ( slope.Z | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("slope.Z", "NTB")) %>% plot()
```

```{r}
m <- glmer( as.factor(ingChoiceN) ~ scale(desirability)*scale(RSE) + ( scale(desirability) | subID) + ( scale(desirability) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("desirability", "RSE")) %>% plot()

m <- glmer( as.factor(ingChoiceN) ~ scale(desirability)*scale(SCC) + ( scale(desirability) | subID) + ( scale(desirability) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("desirability", "SCC")) %>% plot()

m <- glmer( as.factor(ingChoiceN) ~ scale(desirability)*scale(DS) + ( scale(desirability) | subID) + ( scale(desirability) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("desirability", "DS")) %>% plot()

m <- glmer( as.factor(ingChoiceN) ~ scale(desirability)*scale(NFC) + ( scale(desirability) | subID) + ( scale(desirability) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("desirability", "NFC")) %>% plot()

m <- glmer( as.factor(ingChoiceN) ~ scale(desirability)*scale(SING.Ind) + ( scale(desirability) | subID) + ( scale(desirability) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("desirability", "SING.Ind")) %>% plot()

m <- glmer( as.factor(ingChoiceN) ~ scale(desirability)*scale(SING.Inter) + ( scale(desirability) | subID) + ( scale(desirability) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("desirability", "SING.Inter")) %>% plot()

m <- glmer( as.factor(ingChoiceN) ~ scale(desirability)*scale(Proto) + ( scale(desirability) | subID) + ( scale(desirability) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("desirability", "Proto")) %>% plot()

m <- glmer( as.factor(ingChoiceN) ~ scale(desirability)*scale(SI) + ( scale(desirability) | subID) + ( scale(desirability) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("desirability", "SI")) %>% plot()

m <- glmer( as.factor(ingChoiceN) ~ scale(desirability)*scale(NTB) + ( scale(desirability) | subID) + ( scale(desirability) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("desirability", "NTB")) %>% plot()
```

```{r}
m <- glmer( as.factor(ingChoiceN) ~ predicted.Z*novel*scale(NFC) + ( predicted.Z+novel | subID) + ( SE.Z*as.factor(novel) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("SE", "novel","RSE")) %>% plot()
```

```{r}
m <- glmer( as.factor(ingChoiceN) ~ SE.Z*as.factor(novel)*scale(SCC) + ( SE.Z+as.factor(novel) | subID) + ( SE.Z+as.factor(novel) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("SE","novel","SCC")) %>% plot()
```

```{r}
m <- glmer( as.factor(ingChoiceN) ~ SE.Z*as.factor(novel)*scale(DS) + ( SE.Z+as.factor(novel) | subID) + ( SE.Z+as.factor(novel) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("SE","novel","DS")) %>% plot()
```

```{r}
m <- glmer( as.factor(ingChoiceN) ~ SE.Z*as.factor(novel)*scale(NFC) + ( SE.Z+as.factor(novel) | subID) + ( SE.Z+as.factor(novel) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("SE","novel", "NFC")) %>% plot()
```

```{r}
m <- glmer( as.factor(ingChoiceN) ~ SE.Z*as.factor(novel)*scale(SING.Ind) + ( SE.Z+as.factor(novel) | subID) + ( SE.Z+as.factor(novel) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("SE","novel","SING.Ind")) %>% plot()
```

```{r}
m <- glmer( as.factor(ingChoiceN) ~ SE.Z*as.factor(novel)*scale(SING.Inter) + ( SE.Z+as.factor(novel) | subID) + ( SE.Z+as.factor(novel) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("SE","novel","SING.Inter")) %>% plot()
```

```{r}
m <- glmer( as.factor(ingChoiceN) ~ SE.Z*as.factor(novel)*scale(Proto) + ( SE.Z+as.factor(novel) | subID) + ( SE.Z+as.factor(novel) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("SE", "Proto")) %>% plot()
```

```{r}
m <- glmer( as.factor(ingChoiceN) ~ SE.Z*as.factor(novel)*scale(SI) + ( SE.Z+as.factor(novel) | subID) + ( SE.Z+as.factor(novel) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("SE","novel","SI")) %>% plot()
```

```{r}
m <- glmer( as.factor(ingChoiceN) ~ SE.Z*as.factor(novel)*scale(NTB) + ( SE.Z+as.factor(novel) | subID) + ( SE.Z+as.factor(novel) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("SE", "novel", "NTB")) %>% plot()
```


